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step of forecasting






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  • Decisions made by using forecasting technique are more accurate than those made on the basis of “gut” feelings.
  • Most forecast go wrong bcos it is futuristic. Underestimation, oversestimation

step of forecasting step of forecasting Presentation Transcript

  • Steps Of Forecasting
    Group 2
  • Determine the use of the forecast
    Who needs the forecast?
    All organizations operate in the atmosphere of uncertainty.
    Decisions to be made affects future of the organization.
  • Select the items to be forecasted
    The item to be forecasted.
    Dependent variable to be studied.
  • Determine the time horizon of the forecast
    Short-range forecast
    Up to 1 year
    Purchasing, job scheduling, job assignments
    Medium-range forecast
    1 year to 3 years
    Sales and production planning
    Long-range forecast
    3+ years
    New product planning, research and development
  • Select Forecasting approach
    Qualitative Methods
    Used when situation is vague and little data exist
    New products
    New technology
    Involves intuition, experience
  • Quantitative Methods
    Used when situation is ‘stable’ and historical data exist
    Existing products
    Current technology
    Involves mathematical techniques
  • Data collection
    One of the most difficult and time consuming part of forecasting is the collection of valid and reliable data. Forecast can be no more accurate than the data on which it is based
    Data can be collected from- primary source and secondary source
  • Four criteria can be applied to the determination of whether the data will be useful-
    Data should be reliable and accurate
    Data should be relevant
    Data should be consistent
    Data should be timely
  • Data Reduction
    Since available data can be either too much or too less, data reduction is necessary.
    Decide which data is most complete, valid and reliable to increase data accuracy.
    Some times accurate data may be available but only in certain historic periods.
  • Exploring Time Series Data Patterns
    Horizontal pattern- When data observation fluctuate around a constant level or mean
    Trend pattern- When data observation grow or decline over an extended period of time
    Cyclic pattern- When data observation exhibits rises and falls that are not of a fixed period
    Seasonal Pattern- When data observation are influenced by seasonal factors.
  • Exploring Data Patterns with Auto correlation Analysis
    Autocorrelation is the correlation between a variable lagged one or more period itself.
    It is used to detect non randomness of data
    To identify an appropriate time series model if data is not random
  • Y= 1704/12 = 142
    r1 = 843/1474 = .572
  • Select the forecasting model(s)
    The most prominently used models are:
    Exponential smoothing method with 1 or 2 variables.
    Regression Models
    Once the model has been judicially selected, its parameters are estimated for model fitting purposes.
  • Make the forecast
    Forecast is made for a particular period.
  • Forecast evaluation
    Comparing Forecast value with actual historical values.
    • ……………………… ^
    Error : et = yt –y t
  • Thank you